import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
import plotly
import datetime as dt

df = pd.read_csv('flightdata.csv')
print(len(df))
df.head()

28996563

	callsign	origin	destination	day
0	CES597	YBBN	CYVR	2019-10-01 00:00:00+00:00
1	ETH3640	VHHH	EBMB	2019-10-01 00:00:00+00:00
2	ETH628	KEWR	VTBS	2019-10-01 00:00:00+00:00
3	AMX023	SAEZ	LEMD	2019-10-01 00:00:00+00:00
4	RAM208	SBGR	CYUL	2019-10-01 00:00:00+00:00

id_count = df['origin'].value_counts()
id_df = pd.DataFrame(id_count)
id_df = id_df[id_df.origin > 10000]
id_df.head(20)

	origin
KORD	512214
KATL	480633
KDFW	405724
KDEN	384610
KLAX	376336
KLAS	322010
KPHX	311131
KCLT	309693
KSEA	289971
KSFO	230016
KBOS	228509
KPHL	223126
KMSP	220727
KEWR	212623
EHAM	205968
KMIA	205157
EDDF	204575
KJFK	198515
KDTW	193545
LFPG	190589

id_count = df['destination'].value_counts()
id_df = pd.DataFrame(id_count)
id_df = id_df[id_df.destination > 10000]
id_df.head(20)

	destination
KORD	483433
KATL	432216
KDFW	353756
KLAX	337861
KLAS	303932
KPHX	296740
KSEA	278417
KCLT	269319
KDEN	264810
KSFO	222455
KBOS	219651
KPHL	207955
KEWR	201202
KMIA	196539
KJFK	195086
EHAM	192198
EDDF	191562
KMSP	187764
EGLL	186479
LFPG	186219

It could be observed that the top 20 airports with the most departing and arriving flights doesn't vary much. So to simplify the problem, the airports are chosen based on the number of departing flights.

First, a general study on the number of flights departing and arriving at each airport is done. The first 20 airports with the most departing flights are shown below. It could be observed that the top 10 airports with most departing flights are all US airports, whose ICAO code starts with 'K'. xxx So three areas are divided to analyse...

# America without US
df_Ame_ori = df[df['origin'].str.startswith('B')
                | df['origin'].str.startswith('C')
                | df['origin'].str.startswith('PA')
#                | df['origin'].str.startswith('K')
                | df['origin'].str.startswith('M')
                | df['origin'].str.startswith('T')
                | df['origin'].str.startswith('S')]
df_Ame_ori.head(10)

	callsign	origin	destination	day
3	AMX023	SAEZ	LEMD	2019-10-01 00:00:00+00:00
4	RAM208	SBGR	CYUL	2019-10-01 00:00:00+00:00
11	CMP382	SBGR	KSFO	2019-10-01 00:00:00+00:00
28	AVA020	SBGR	KJFK	2019-10-01 00:00:00+00:00
71	CFG235	MROC	EDDF	2019-10-01 00:00:00+00:00
73	AIJ2820	CYVR	CYUL	2019-10-01 00:00:00+00:00
87	WJA2311	CYYZ	CYYC	2019-10-01 00:00:00+00:00
92	ACA015	CYYZ	VHHH	2019-10-01 00:00:00+00:00
102	NRS7506	SAEZ	EGKK	2019-10-01 00:00:00+00:00
112	CMP470	SAEZ	CYYZ	2019-10-01 00:00:00+00:00

#US
df_US_ori = df[df['origin'].str.startswith('K')]
df_US_ori.head(10)

	callsign	origin	destination	day
2	ETH628	KEWR	VTBS	2019-10-01 00:00:00+00:00
6	QFA7554	KORD	YMML	2019-10-01 00:00:00+00:00
10	UAE9252	KORD	YSSY	2019-10-01 00:00:00+00:00
15	SIA37	KLAX	WSSS	2019-10-01 00:00:00+00:00
21	CCA933	KLAX	LFPG	2019-10-01 00:00:00+00:00
33	EVA619	KLAX	RCTP	2019-10-01 00:00:00+00:00
47	CAL5167	KLAX	RCTP	2019-10-01 00:00:00+00:00
63	AAR281	KLAX	RKSI	2019-10-01 00:00:00+00:00
82	N191WT	KFTY	KSAN	2019-10-01 00:00:00+00:00
90	N4308S	KSTS	KSUS	2019-10-01 00:00:00+00:00

id_AO_count = df_Ame_ori['origin'].value_counts()
id_df_AO = pd.DataFrame(id_AO_count)
id_df_AO = id_df_AO[id_df_AO.origin > 10000]
id_df_AO.head(10)

	origin
CYYZ	134386
PANC	119452
CYVR	86232
CYUL	64690
CYYC	64222
SBGR	52522
TJSJ	40115
SBBR	31673
SBSP	30818
SBRJ	29866

id_USO_count = df_US_ori['origin'].value_counts()
id_df_USO = pd.DataFrame(id_USO_count)
id_df_USO = id_df_USO[id_df_USO.origin > 10000]
id_df_USO.head(10)

	origin
KORD	512214
KATL	480633
KDFW	405724
KDEN	384610
KLAX	376336
KLAS	322010
KPHX	311131
KCLT	309693
KSEA	289971
KSFO	230016

Five US airports and five other airports in the Americas which have the most departing flights are under consideration: KORD, KATL, KDFW, KDEN, KLAX, CYYZ, PANC, CYVR, CYUL, CYYC.

df_Am = df[(df['origin'] == 'KORD') |
           (df['origin'] == 'KATL') |
           (df['origin'] == 'KDFW') |
           (df['origin'] == 'KDEN') |
           (df['origin'] == 'KLAX') |
           (df['origin'] == 'CYYX') |
           (df['origin'] == 'PANC') |
           (df['origin'] == 'CYVR') |
           (df['origin'] == 'CYUL') |
           (df['origin'] == 'CYYC') ]
df_Am.head()
len(df_Am)

2494113

df_group = df_Am.groupby(['origin', 'day'], as_index=False).count()
df_group.head()

	origin	day	callsign	destination
0	CYUL	2019-10-01 00:00:00+00:00	156	156
1	CYUL	2019-10-02 00:00:00+00:00	173	173
2	CYUL	2019-10-03 00:00:00+00:00	183	183
3	CYUL	2019-10-04 00:00:00+00:00	192	192
4	CYUL	2019-10-05 00:00:00+00:00	164	164

fig = px.line(df_group , x="day", y="callsign", color="origin")
fig.update_xaxes(title={'text': 'Time'})
fig.update_yaxes(title={'text': 'Number of flights'})
fig.show()